pyhpc-benchmarks VS MATDaemon.jl

Compare pyhpc-benchmarks vs MATDaemon.jl and see what are their differences.

pyhpc-benchmarks

A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python :rocket: (by dionhaefner)
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pyhpc-benchmarks MATDaemon.jl
6 2
301 24
- -
3.2 5.6
4 months ago 2 months ago
Python Julia
The Unlicense MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

pyhpc-benchmarks

Posts with mentions or reviews of pyhpc-benchmarks. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-12-05.

MATDaemon.jl

Posts with mentions or reviews of MATDaemon.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-08.

What are some alternatives?

When comparing pyhpc-benchmarks and MATDaemon.jl you can also consider the following projects:

tf-quant-finance - High-performance TensorFlow library for quantitative finance.

Oceananigans.jl - 🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs

pyopencl - OpenCL integration for Python, plus shiny features

XLA.jl - "Maybe we have our own magic."

sqloxide - Python bindings for sqlparser-rs

Mex.jl - Embedding Julia in the MATLAB process.

3d-ken-burns - an implementation of 3D Ken Burns Effect from a Single Image using PyTorch

julia - The Julia Programming Language

XLA.jl - Julia on TPUs

PSyclone - Domain-specific compiler and code transformation system for Finite Difference/Volume/Element Earth-system models in Fortran

torchquad - Numerical integration in arbitrary dimensions on the GPU using PyTorch / TF / JAX